Methods and systems for characterizing tissue including tissue types within tissue are known (See, for example, U.S. Pat. Nos. 6,200,268; 6,381,350; 7,074,188; 7,175,597; 7,215,802; 7,359,554; 7,463,759; 7,627,156; 7,789,834; 7,874,990; 7,899,224; 7,927,275; 7,940,969; and 7,978,916; each of which is hereby incorporated by reference in its entirety). In these previous methods and systems, tissue is scanned with an imaging device, (e.g., an intravascular ultrasound (IVUS) or optical coherence tomography (OCT) device) and backscatter signal data is collected. Histology images are prepared and digitized which correspond to the scanned vascular sections. A tissue type is selected on the histology image and its coordinates are mapped to a corresponding location on the IVUS or OCT image constructed from the backscatter signal. The image location is then translated to the corresponding signal section of the raw backscatter signal. Frequency analysis is performed on the signal section to determine its signal properties. The signal properties are correlated to the selected tissue type of the histology image and stored in a tissue characterization database. The process is then repeated for all tissue types and other tissue types found within tissue and stored in the database. The components of tissue such as tissue can be identified directly from the raw backscatter signal by matching its signal properties with the signal properties of the database, thus, identifying tissue types or other tissue components in vivo and in real-time.
Classification trees are used to solve problems in areas as diverse as target marketing, fraud detection, pattern recognition, computer vision, and medical diagnosis. In many applications, classification trees are carefully designed once but then applied to many data sets to provide automated classifications. This approach is used to create validated classifiers for tissue classification in mammography and intravascular ultrasound diagnostic procedures. While training the classifier is done offline, tree evaluation of each patient's data in these applications is an on-line algorithm where a user waits for a classification to be performed on many, many samples. Time spent waiting for this evaluation consumes valuable procedure room equipment and personnel. Performance requirements only increase when single images are replaced by moving video for computer vision applications, as in robotic navigation. In this environment, many classifications are needed in real-time to compute and affect a timely response. Thus the need for high-performance on-line evaluation of classification trees ranges from beneficial to absolutely necessary.